Artificial Intelligence Certification Certified By Microsoft.
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Executive Program in Artificial Intelligence Technology Certified by Microsoft.
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01 Learning Path Introduction to Python
Data Science Fundamentals
Concepts of Python Programming
Introduction to Data Science
Configuration of Development Environment
Real World Use-Cases of Data Science
Variable and Strings
Walkthrough of Data Types
Functions, Control Flow and Loops
Data Science Project Lifecycle
Tuple, Lists and Dictionaries Standard Libraries
Introduction to NumPy Basics of NumPy Arrays Mathematical Operations in NumPy NumPy Array Manipulation NumPy Array Broadcasting
02 Learning Path Data Manipulation with Pandas Data Structures in Pandas-Series and Data Frames Data Cleaning in Pandas Data Manipulation in Pandas
Handling Missing Values in Datasets Hands-on: Implement NumPy Arrays and Pandas Data Frames
Data Visualization in Python Plotting Basic Charts in Python Data Visualization with Matplotlib Statistical Data Visualization with Seaborn
Hands-on: Coding Sessions Using Matplotlib, Seaborn Package
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Exploratory Data Analysis Introduction to Exploratory Data Analysis (EDA) Steps Plots to Explore Relationship Between Two Variables
Histograms, Box plots to Explore a Single Variable Heat Maps, Pair plots to Explore Correlations
03 Learning Path Introduction to Machine Learning
Linear Regression
What is Machine Learning?
Introduction to Linear Regression
Use Cases of Machine Learning
Use Cases of Linear Regression
Types of Machine Learning - Supervised to
How to Fit a Linear Regression Model?
Unsupervised methods Machine Learning Workflow
Logistic Regression
Evaluating and Interpreting Results from Linear Regression Models Predict Bike Sharing Demand
Introduction to Logistic Regression Logistic Regression Use Cases Understand Use of odds & Logic Function to Perform Logistic Regression Predicting Credit card Default Cases
04 Learning Path Decision Trees & Random Forest Introduction to Decision Trees & Random Forest Understanding Criterion (Entropy & Information Gain) used in Decision Trees Using Ensemble Methods in Decision Trees Applications of Random Forest
Dimensionality Reduction using PCA Introduction to Curse of Dimensionality What is Dimensionality Reduction? Technique Used in PCA to Reduce Dimensions Applications of Principle Component Analysis
(PCA) Optimize Model Performance using PCA on SPECTF heartdata 5
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Model Evaluation Techniques Introduction to Evaluation Metrics and Model Selection in Machine Learning Importance of Confusion Matrix for Predictions Measures of Model Evaluation - Sensitivity, Specificity, Precision, Recall & f-score Use AUC-ROC Curve to Decide Best Model
05 Learning Path K-NearestNeighbours Introduction to K-NN Calculate Neighbours using Distance Measures Find Optimal Value of K in K-NN Method Advantage & Disadvantages of K-NN
K-Means Clustering Introduction to K-Means Clustering Decide Clusters by Adjusting Centroids Understand Applications of Clustering in Machine Learning Segment Hands in Pokerdata
Naive Bayes Classifier Introduction to Naïve Bayes Classification Refresher on Probability Theory Applications of Naive Bayes Algorithm in Machine Learning Classify Spam Emails Based on Probability
Support Vector Machines Introduction to SVM Figure Decision Boundaries Using Support Vectors Identify Hyperplane in SVM Applications of SVM in Machine Learning
06 Learning Path Time Series Forecasting Components of Time Series Data Interpreting Autocorrelation & Partial Autocorrelation Functions Introduction to Time Series Analysis Stationary Vs Non Stationary Data Stationary data and Implement ARIMA model
Recommendation Systems Introduction to Recommender Systems Types of Recommender Systems -
Collaborative, Content Based & Hybrid Types of Similarity Matrix (Cosine, Jaccard, Pearson Correlation) Segment Hands in Poker DataBuild Recommender systems on Movie data using K-NN Basics
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Apriori Algorithm Applications of Apriori algorithm Understand Association rule Developing product Recommendations using Association Rules Analyse Online Retail Data using Association Rules
07 Learning Path Linear Discriminant Analysis
Recap of Dimensionality Reduction Concepts Types of Dimensionality Reduction Dimensionality Reduction Using LDA Apply LDA to Determine Wine Quality
Ensemble Learning Introduction to Ensemble Learning
What are Bagging and Boosting techniques? What is Bias Variance Trade Off? Predict Wage (annual income) Classes from Adult Census Data
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Anomaly Detection Introduction to Anomaly Detection How Anomaly Detection Works? Types of Anomaly Detection: Density Based, Clustering etc. NET Based Commands Detect Anomalies on Electrocardiogram Data
Artificial Intelligence
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